Nathan P. Lawrence

Publication Activity (10 Years)

Years Active: 2020-2024
Publications (10 Years): 18

Top Topics

Function Approximation

Reinforcement Learning

Industrial Process

Behavioural Cloning

Top Venues

Publications

Nathan P. Lawrence, Philip D. Loewen, Shuyuan Wang, Michael G. Forbes, R. Bhushan Gopaluni
Deep Hankel matrices with random elements. L4DC (2024)
Nathan P. Lawrence, Seshu Kumar Damarla, Jong Woo Kim, Aditya Tulsyan, Faraz Amjad, Kai Wang, Benoît Chachuat, Jong Min Lee, Biao Huang, R. Bhushan Gopaluni
Machine learning for industrial sensing and control: A survey and practical perspective. CoRR (2024)
Nathan P. Lawrence, Philip D. Loewen, Shuyuan Wang, Michael G. Forbes, R. Bhushan Gopaluni
Deep Hankel matrices with random elements. CoRR (2024)
Nathan P. Lawrence, Philip D. Loewen, Shuyuan Wang, Michael G. Forbes, R. Bhushan Gopaluni
Stabilizing reinforcement learning control: A modular framework for optimizing over all stable behavior. Autom. 164 (2024)
Nathan P. Lawrence, Philip D. Loewen, Shuyuan Wang, Michael G. Forbes, R. Bhushan Gopaluni
Stabilizing reinforcement learning control: A modular framework for optimizing over all stable behavior. CoRR (2023)
Tobi Michael Alabi, Nathan P. Lawrence, Lin Lu, Zaiyue Yang, R. Bhushan Gopaluni
Automated deep reinforcement learning for real-time scheduling strategy of multi-energy system integrated with post-carbon and direct-air carbon captured system. CoRR (2023)
Nathan P. Lawrence, Philip D. Loewen, Shuyuan Wang, Michael G. Forbes, R. Bhushan Gopaluni
A modular framework for stabilizing deep reinforcement learning control. CoRR (2023)
Shuyuan Wang, Philip D. Loewen, Nathan P. Lawrence, Michael G. Forbes, R. Bhushan Gopaluni
Reinforcement Learning with Partial Parametric Model Knowledge. CoRR (2023)
Daniel G. McClement, Nathan P. Lawrence, Johan U. Backström, Philip D. Loewen, Michael G. Forbes, R. Bhushan Gopaluni
Meta Reinforcement Learning for Adaptive Control: An Offline Approach. CoRR (2022)
Daniel G. McClement, Nathan P. Lawrence, Michael G. Forbes, Philip D. Loewen, Johan U. Backström, R. Bhushan Gopaluni
Meta-Reinforcement Learning for Adaptive Control of Second Order Systems. CoRR (2022)
R. Bhushan Gopaluni, Aditya Tulsyan, Benoît Chachuat, Biao Huang, Jong Min Lee, Faraz Amjad, Seshu Kumar Damarla, Jong Woo Kim, Nathan P. Lawrence
Modern Machine Learning Tools for Monitoring and Control of Industrial Processes: A Survey. CoRR (2022)
Nathan P. Lawrence, Michael G. Forbes, Philip D. Loewen, Daniel G. McClement, Johan U. Backström, R. Bhushan Gopaluni
Deep Reinforcement Learning with Shallow Controllers: An Experimental Application to PID Tuning. CoRR (2021)
Daniel G. McClement, Nathan P. Lawrence, Philip D. Loewen, Michael G. Forbes, Johan U. Backström, R. Bhushan Gopaluni
A Meta-Reinforcement Learning Approach to Process Control. CoRR (2021)
Nathan P. Lawrence, Philip D. Loewen, Michael G. Forbes, Johan U. Backström, R. Bhushan Gopaluni
Almost Surely Stable Deep Dynamics. CoRR (2021)
Nathan P. Lawrence, Philip D. Loewen, Michael G. Forbes, Johan U. Backström, R. Bhushan Gopaluni
Almost Surely Stable Deep Dynamics. NeurIPS (2020)
Steven Spielberg, Aditya Tulsyan, Nathan P. Lawrence, Philip D. Loewen, R. Bhushan Gopaluni
Deep Reinforcement Learning for Process Control: A Primer for Beginners. CoRR (2020)
Nathan P. Lawrence, Gregory E. Stewart, Philip D. Loewen, Michael G. Forbes, Johan U. Backström, R. Bhushan Gopaluni
Reinforcement Learning based Design of Linear Fixed Structure Controllers. CoRR (2020)
Nathan P. Lawrence, Gregory E. Stewart, Philip D. Loewen, Michael G. Forbes, Johan U. Backström, R. Bhushan Gopaluni
Optimal PID and Antiwindup Control Design as a Reinforcement Learning Problem. CoRR (2020)